Secure short-term GPU capacity for ML workloads with EC2 Capacity Blocks for ML and SageMaker training plans

2026-05-07 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

AWS offers solutions to secure short-term GPU capacity for machine learning workloads, addressing the industry-wide scarcity of GPUs. For planned, steady-state workloads, On-Demand Capacity Reservations (ODCRs) exist, but they are often limited for GPU instances and lack cost advantages for short-term use. This article introduces Amazon EC2 Capacity Blocks for ML and Amazon SageMaker training plans as alternatives for short-term GPU needs. EC2 Capacity Blocks allow reserving GPU capacity for 1-182 days, up to eight weeks in advance, with discounts of 40-50% compared to on-demand rates, supporting up to 256 instances across multiple blocks. SageMaker training plans offer reserved GPU capacity for SageMaker-managed environments like training jobs, HyperPod clusters, and inference, providing 70-75% discounts. Both options require upfront payment and are designed for specific use cases, with a decision framework based on infrastructure management, availability, and cost.

Key takeaway

For MLOps Engineers or AI Architects planning short-term GPU-intensive tasks like model validation or load testing, you should evaluate whether your workload requires direct EC2 control or a managed SageMaker environment. Opt for EC2 Capacity Blocks for ML if you need full OS/networking control, or Amazon SageMaker training plans for SageMaker-managed services, to secure discounted, guaranteed GPU capacity for specific time windows and avoid availability issues.

Key insights

AWS provides specialized services for reserving short-term GPU capacity, offering cost savings and guaranteed availability for ML workloads.

Principles

• Prioritize on-demand capacity first.
• Match capacity reservation to workload environment.
• Upfront payment secures discounted rates.

Method

Evaluate GPU capacity needs based on infrastructure control (EC2 vs. SageMaker), then attempt on-demand, and finally reserve capacity using Capacity Blocks for EC2 or SageMaker training plans for managed ML workloads.

In practice

• Use Capacity Blocks for direct EC2 GPU control.
• Utilize SageMaker training plans for managed ML services.
• Consider Spot Instances for interrupt-tolerant workloads.

Topics

• GPU Capacity
• Machine Learning Workloads
• EC2 Capacity Blocks for ML
• SageMaker Training Plans
• AWS Compute Resources

Best for: Machine Learning Engineer, MLOps Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.